Amazon Technologies, Inc.

Leo Parker Dirac

Nicolle M Correa

Aleksandr Mikhaylovich Ingerman

Jin Li

Swaminathan Sivasubramanian

Nafea Bshara

Sudipta Sengupta

Andrea Olgiati

Poorna Chand Srinivas Perumalla

Matthew James Wood

Bharathan Balaji

Dominic Rajeev Divakaruni

Tianming Zheng

Bratin Saha

Stefano Stefani

Sean Michael Scott

Saurabh Gupta

Blair Livingstone Hotchkies

Sunil Mallya Kasaragod

Vineet Khare

Anthony T Chor

Paul Christopher Cerda

Sahika Genc

Saman Zarandioon

Min Chong

Bradley Scott Bowman

Udip Pant

Kevin Andrew Granade

Daniel Blick

Sriram Krishnan

Oleg Rybakov

Vijai Mohan

Robert Matthias Steele

Donghui Zhuo

Sudhakar Rao Puvvadi

Orchid Majumder

Thomas Albert Faulhaber, Jr

Gurumurthy Swaminathan

Rejith George Joseph

Avinash Aghoram Ravichandran

Vladimir Zhukov

Rakesh Madhavan Nambiar

Tanya Bansal

Laurence Louis Eric Rouesnel

Charles Eric Dannaker

Tarun Agarwal

Eric Li Sun

Ravikumar Anantakrishnan Venkateswar

Patricia Grao Gil

Philip Gautier

Piali Das

Marthinus Coenraad De Clercq Wentzel

Fan Li

Srikanth Thirumalai

Rahul Sharma

Edo Liberty

Waqas Syed Ahmed

David Robert Thomas

Fabian Moerchen

Zohar Karnin

James Joseph Jesensky

Gowda Dayananda Anjaneyapura Range

Blake Parker Hess

Xiong Zhou

Taylor Goodhart

Lakshmi Naarayanan Ramakrishnan

Ragav Venkatesan

Kevin Michael Small

Matias Omar Gregorio Benitez

Rodolphe Jenatton

Ralf Herbrich

Jalaja C Kurubarahalli

Steven Andrew Loeppky

Pragya Agarwal

Aditya Rekha Dharanipragada

Rakesh Ramakrishnan

Shariq Siddiqui

Urvashi Chowdhary

Michael Brueckner

Maksim Lapin

Rajankumar Singh

Karan Hemant Kothari

Polly Po Yee Lee

Hannes Scheidler

Jun Qian

Aman Agarwal

Alejandro Fonseca

Samuel Oshin

Cory Pruce

Jun Wu

Eftiquar Shaikh

Sahika Gene

Daniel Evans

Umang Wadhwa

Tuhin Sarkar

Mark Klunder

Zdravko Pantic

Animashree Anandkumar

Brian James Townsend

A simulation application container executes a simulation of a system in a simulation environment, through which an agent representing the system uses a reinforcement learning model to operate within the simulation environment. The simulation application container obtains data indicating how the agent performed in the simulation environment and transmits this data to a robot application container. The robot application container uses the data to update the reinforcement learning model and provides the updated reinforcement learning model to perform another iteration of the simulation and continue training the reinforcement learning model.

Simulation modeling exchange

At a machine learning service, a determination is made that an analysis to detect whether at least a portion of contents of one or more observation records of a first data set are duplicated in a second set of observation records is to be performed. A duplication metric is obtained, indicative of a non-zero probability that one or more observation records of the second set are duplicates of respective observation records of the first set. In response to determining that the duplication metric meets a threshold criterion, one or more responsive actions are initiated, such as the transmission of a notification to a client of the service.

Efficient duplicate detection for machine learning data sets

A machine learning environment utilizing training data generated by customer networks. A reinforcement learning machine learning environment receives and processes training data generated by simulated hosted, or integrated, customer networks. The reinforcement learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the integrated customer networks. The customer networks include an agent process that collects training data and forwards the training data to the machine learning clusters. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configure the application of the reinforcement learning machine learning processes.

Integrated machine learning training

Features related to systems and methods for reinforcement learning are described. The environment includes one or more agents for automating the training of reinforcement learning (RL) models. The environment may include a simulator or real-world observations. The features described identify key training parameters, resource configurations, virtual network configurations, and simulators based on historical training data.

Dynamic environment configurations for machine learning services

A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

Machine learning service

Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Automated machine learning pipeline exploration and deployment

At a first resource to be used to perform a computing operation, a pair of execution environments is configured. I/O permissions of programs running in the different environments are based on respective sets of constraints. A program performs the operation in one of the environments, with input data being provided to the program from the second environment. A result of the operation is provided to a destination from the second environment.

Isolation techniques at execution platforms used for sensitive data analysis

Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes attaching a first set of one or more accelerator slots of an accelerator appliance to an application instance of a multi-tenant provider network according to an application instance configuration, the application instance configuration to define per accelerator slot capabilities to be used by an application of the application instance, wherein the multi-tenant provider network comprises a plurality of computing devices configured to implement a plurality of virtual compute instances, and wherein the first set of one or more accelerator slots is implemented using physical accelerator resources accessible to the application instance; while performing inference using the loaded machine learning model of the application using the first set of one or more accelerator slots on the attached accelerator appliance, managing resources of the accelerator appliance using an accelerator appliance manager of the accelerator appliance.

Fault-tolerant accelerator based inference service

Methods, apparatuses, and systems for a web services provider to interact with a client on remote job execution. For example, a web services provider may receive a job command, from an interactive programming environment of a client, applicable to job for a machine learning algorithm on a web services provider system, process the job command using at least one of a training instance and an inference instance, and provide metrics and log data during the processing of the job to the interactive programming environment.

Using a client to manage remote machine learning jobs

A machine learning environment utilizing training data generated by customer environments. A reinforced learning machine learning environment receives and processes training data generated by independently hosted, or decoupled, customer environments. The reinforced learning machine learning environment corresponds to machine learning clusters that receive and process training data sets provided by the decoupled customer environments. The customer environments include an agent process that collects training data and forwards the training data to the machine learning clusters without exposing the customer environment. The machine learning clusters can be configured in a manner to automatically process the training data without requiring additional user inputs or controls to configured the application of the reinforced learning machine learning processes.

Decoupled machine learning training

Techniques for evaluating an output of a machine learning model and using the evaluation to retrain the machine learning model are described. For example, a data set that is output from a layer of the machine learning model is reduced to a 2-D or 3-D representation that is suitable for viewing. A user views the reduced data set in a viewing environment such as virtual reality or augmented reality. The user makes changes using that viewing environment. The changes are then used to retrain the machine learning model.

Virtual/augmented reality data evaluation

A simulation management service receives a request to perform reinforcement learning for a robotic device. The request can include computer-executable code defining a reinforcement function for training a reinforcement learning model for the robotic device. In response to the request, the simulation management service generates a simulation environment and injects the computer-executable code into a simulation application for the robotic device. Using the simulation application and the computer-executable code, the simulation management service performs the reinforcement learning within the simulation environment.

Reinforcement learning model training through simulation

Techniques for sequence to sequence (S2S) model building and/or optimization are described. For example, a method of receiving a request to build a sequence to sequence (S2S) model for a use case, wherein the request includes at least a training data set, generating parts of a S2S algorithm based on the at least one use case, determined parameters, and determined hyperparameters, and training a S2S algorithm built from the parts of the S2S algorithm using the training data set to generate the S2S model is detailed.

Building sequence to sequence (S2S) models using previously generated S2S models with similar use cases

A first set of execution platforms is deployed for a set of operations of a training phase of a machine learning model. Prior to the completion of the training phase, a triggering condition for deployment of a different set of execution platforms is detected. The different set of execution platforms is deployed for a subsequent set of training phase operations.

Dynamically scaled training fleets for machine learning

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Seattle, WA, United States of America

Leo Parker Dirac